The posting
Conviva is the experience analytics platform for AI agents, apps, and streaming. Conviva pioneered quality of experience measurement for streaming video and has supported live digital experiences across 20 Super Bowls, 10 Olympic Games, and five FIFA World Cup tournaments. With full-census, real-time, stateful analytics, Conviva shows what consumers actually experience, not just whether systems are running, and connects that experience to revenue, retention, and risk.
We are looking for an AI/ML Engineer with strong machine learning fundamentals and hands-on experience in model development, training, and evaluation. In this role, you will work on challenging real-world problems across machine learning and AI, taking ideas from research and experimentation to practical solutions. You will own key parts of the ML development lifecycle, including data preparation, model design, training, evaluation, debugging, and iteration.
We are looking for someone who goes beyond implementing existing approaches—someone who can understand a problem deeply, evaluate alternative solutions, make informed technical decisions, and systematically investigate why a model succeeds or fails.
You will collaborate closely with engineering and product teams to explore new ML approaches, develop effective models, and build the tools and infrastructure needed to support scalable experimentation and deployment. We welcome candidates from both academic research and industry backgrounds, including recent PhD graduates with strong hands-on research experience and engineers who have demonstrated exceptional model development capabilities in industry.
What Success Will Look Like:
- Design, develop, train, and evaluate machine learning models to solve complex real-world problems.
- Own the end-to-end ML development lifecycle, including dataset construction, model design, training, experimentation, evaluation, and continuous improvement.
- Explore and apply modern ML techniques, including deep learning, reinforcement learning, and LLM training or fine-tuning, as appropriate to the problem.
- Design rigorous experiments, define meaningful evaluation metrics, establish baselines, and analyze model performance to guide technical decisions.
- Translate research ideas into practical solutions through close collaboration with engineering and product teams.
- Contribute to ML tooling, data pipelines, and infrastructure that support efficient experimentation and scalable model development.
- Take ownership of technical challenges, proactively investigate new approaches, and continuously improve model quality and reliability.
- Collaborate effectively in a small, highly technical team, sharing knowledge and contributing to engineering best practices.
Who You Are & What You've Done
- PhD preferred in Computer Science, Machine Learning, Mathematics, Statistics, or a related quantitative field. Recent PhD graduates with strong research experience are encouraged to apply.
- Strong foundation in machine learning, deep learning, probability, statistics, algorithms, and data structures.
- Hands-on experience developing and training ML models through academic research or industry projects.
- Experience with ML frameworks such as PyTorch or TensorFlow.
- Solid understanding of the ML development lifecycle, including data preparation, model training, experimental design, evaluation methodology, and error analysis.
- Strong programming skills in Python, C++, or another relevant programming language.
- Ability to independently investigate complex technical problems, design experiments, and communicate findings clearly.
- Strong problem-solving skills, intellectual curiosity, and a willingness to explore new techniques.
- Good English communication skills.
Nice to Have
- Research publications at leading conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, or other relevant venues.
- Experience with reinforcement learning, deep learning, LLM fine-tuning, pre-training, or post-training techniques.
- Experience with large-scale model training, distributed training, or ML systems optimization.
- Experience deploying ML models into production or developing ML infrastructure, including model serving, monitoring, and MLOps.
- Familiarity with large-scale data processing frameworks such as Spark or Flink.
The expected salary range for this full-time position is $180,000–$250,000 + equity + benefits. Actual level and compensation are determined by qualifications, professional background, and relevant experience.
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